Water level monitoring using radar remote sensing data: Application to Lake Kivu, central Africa

Water level monitoring using radar remote sensing data: Application to Lake Kivu, central Africa

Physics and Chemistry of the Earth 34 (2009) 722–728 Contents lists available at ScienceDirect Physics and Chemistry of the Earth journal homepage: ...

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Physics and Chemistry of the Earth 34 (2009) 722–728

Contents lists available at ScienceDirect

Physics and Chemistry of the Earth journal homepage: www.elsevier.com/locate/pce

Water level monitoring using radar remote sensing data: Application to Lake Kivu, central Africa Omar Munyaneza a,b,*, Umaru G. Wali a, Stefan Uhlenbrook b,c, Shreedhar Maskey b, McArd J. Mlotha d,1 a

National University of Rwanda, Department of Civil Engineering, P.O. Box 117, Butare, Rwanda UNESCO-IHE Institute for Water Education, P.O. Box 3015, 2601 DA Delft, The Netherlands c Delft University of Technology, Department of Water Resources, P.O. Box 5048, 2600 GA Delft, The Netherlands d Center of Geographic Information System, National University of Rwanda, P.O.Box 117, Butare, Rwanda b

a r t i c l e

i n f o

Article history: Received 16 January 2009 Received in revised form 22 June 2009 Accepted 30 June 2009 Available online 10 July 2009 Keywords: Bathymetry Lake level Radar altimetry Satellite images Lake Kivu Rwanda

a b s t r a c t Satellite radar altimetry measures the time required for a pulse to travel from the satellite antenna to the earth’s surface and back to the satellite receiver. Altimetry on inland lakes generally shows some deviation from in situ level measurements. The deviation is attributed to the geographically varying corrections applied to account for atmospheric effects on radar waves. This study was focused on verification of altimetry data for Lake Kivu (2400 km2), a large inland lake between Rwanda and the Democratic Republic of Congo (DRC) and estimating the lake water levels using bathymetric data combined with satellite images. Altimetry data obtained from ENVISAT and ERS-2 satellite missions were compared with water level data from gauging stations for Lake Kivu. Gauge data for Lake Kivu were collected from the stations ELECTROGAZ and Rusizi. ENVISAT and ERS-2 data sets for Lake Kivu are in good agreement with gauge data having R2 of 0.86 and 0.77, respectively. A combination of the two data sets improved the coefficient of determination to 95% due to the improved temporal resolution of the data sets. The calculated standard deviation for Lake Kivu water levels was 0.642 m and 0.701 m, for ENVISAT and ERS-2 measurements, respectively. The elevation-surface area characteristics derived from bathymetric data in combination with satellite images were used to estimate the lake level gauge. Consequently, the water level of Lake Kivu could be estimated with an RMSE of 0.294 m and an accuracy of ±0.58 m. In situations where gauges become malfunctioning or inaccessible due to damage or extreme meteorological events, the method can be used to ensure data continuity. Crown Copyright Ó 2009 Published by Elsevier Ltd. All rights reserved.

1. Introduction As water being a scarce resource strained by competing demands, it is important to develop and improve the methods which can be used to observe the temporal and spatial variations of water volumes in lakes, rivers and wetlands. At the core of such an observation lies the acquisition of data related to the quality and quantity of water (Fekete and Vorosmarty, 2006). A robust, consistent and reliable method of water quantity measurement is required to effectively monitor the spatial and temporal variation of surface water resources. Rwanda, with a small surface area of 26,338 km2, is characterized by a dense network of lakes, rivers, and wetlands. Approxi* Corresponding author. Address: National University of Rwanda (NUR), Department of Civil Engineering, P.O. Box 117, Butare, Rwanda. Tel.: +31 0641782402; fax: +31 152122921, +250 0788560783. E-mail addresses: [email protected], [email protected] (O. Munyaneza). 1 Present address: Antioch University New England, Center for Tropical Ecology & Conservation (CTEC), 30 Newman Street, 03431 Keene, USA.

mately 10% of the entire country is under water. Lakes occupy about 60%, rivers about 3.5%, and water in wetlands and valleys accounts for about 36.5% (NELSAP, 2007). Most countries operate hydrometric networks for monitoring river discharges, lakes and reservoirs water levels, and groundwater measurements to meet information requirements for the development of water resources. However, the availability and access to hydrological data are limited in developing countries due to the decline of observation stations, fragmented data holdings, and low data quality. Since the mid-eighties, a decline of reporting hydrological stations can be observed in many developing countries mainly due to political and institutional instability as well as economic problems (GRDC, 2006). In Rwanda, some lakes are not gauged continuously because of different reasons like the civil unrest in 1994 and problems of accessing some sites due to topography. In this regard, new techniques should be developed in order to have continuous water level data of Rwandan lakes. A technique of estimating lake water levels by combining information from satellite images with the bathymetric characteristics of the lake may provide a potential

1474-7065/$ - see front matter Crown Copyright Ó 2009 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.pce.2009.06.008

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alternative. Thanks to freely available satellite images, such a technique is also cost effective and could become an indispensable tool for application in developing countries such as Rwanda. Previous bathymetric studies on Lake Kivu by Capart (1960), Tietze (1978), and Mininfra (1998), provide background information highly relevant to lake water level changes. The analysis provided in the 1998 bathymetric maps show that a total volume of 25  109 m3 of water was lost from Lake Kivu from 1960 till 1998 due to increased sediment inflow (Mininfra, 1998). This loss shows that monitoring of lake levels is important and urgently required. Therefore, the objectives of this paper are: (i) to determine the level of accuracy of estimating lake levels from radar images of Lake Kivu; and (ii) to estimate lake gauge water levels using bathymetry data combined with satellite images.

2. Materials and methods 2.1. Study area Lake Kivu is one of the 28 lakes in Rwanda with an estimated volume of water of 500  109 m3 and lies at latitude between 1°240 S and 2°300 S, and longitude between 28°500 E and 29°230 E. Lake Kivu is a freshwater lake and is one of the Great Lakes of Africa. It lies along the border between the Democratic Republic of Congo (DRC) and Rwanda (Fig. 1), and is in the Albertine (Western) Rift, a part of the Great Rift Valley. Lake Kivu covers a surface area of about 2400 km2. The surface area in Rwanda is around 1000 km2. It is located at a height of 1460 m above sea level. The lake bed sits upon a rift valley that is slowly being pulled apart, causing volcanic activity in the area. Lake Kivu is very deep with mean and maximum depths of 240 m and 480 m, respectively. The catchment area of Lake Kivu is about 7000 km2 with maximum length of 84 km and maximum width of 50 km (NELSAP, 2007). Lake Kivu drains into the Rusizi River, which flows southwards into Lake Tanganyika (NELSAP, 2006), which is part of Congo catchment.

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2.2. Data used for the analysis In this research, altimetry data for Lake Kivu from ERS-2 and ENVISAT satellite missions were used together with lake level gauge data. The altimetry data were obtained from ESA Agency satellites, placed on a 35-day repeat orbit. Gauging data were collected from Rusizi station and ELECTROGAZ station on one day basis. There are two ways of acquiring altimetry data. The first which was used in this paper is a completely processed and corrected sea surface height anomaly which the user can directly use for the intended application (NASA, 2005). This is available for the continental ocean surface and can be obtained directly online. ERS, ENVISAT, JASON-1 and TOPEX/POSEIDON (T/P) missions are the most common data sources that provide data on sea level anomalies, dynamic topography, and surface wind speed (NASA, 2005). The satellite derived altimetry data of sea surface heights are organized in ‘‘passes” and ‘‘cycles” (see, e.g. Rosmorduc et al., 2006). A pass is half a revolution of the earth by the satellite from extreme latitude to the opposite extreme latitude. For example for Topex/ Poseidon an ascending (odd numbered passes) pass begins at 66° and ends at +66° (CIT, 2006). A descending (even numbered passes) pass has the opposite sense. A cycle contains a collection of consecutive passes (up to 254 for Topex/Poseidon) and represents a collection of data where the ground track of the satellite repeats itself (10 days for Topex/Poseidon and 35 days for ENVISAT). The time coverage of this data is from 1992 up to the present for both Topex/Poseidon and ENVISAT missions (CIT, 2006). Water level data from gauging stations should fulfill certain requirements to use them for validating altimetry measurements from satellites. First, the gauging stations must be within a very short distance from the satellite ground track. This helps in minimizing measurement errors due to location. These errors may be due to back water effect or tide waves or both. Secondly, the date of measurements of the gauge and altimetry data must match in order to make comparisons (Abebe, 1999). This normally requires daily gauge data from which the data that corresponds to the altimetry passes can be selected for comparison.

Fig. 1. Hydrologic network map of Rwanda with location of Lake Kivu.

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Table 1 Morphometric characteristics of Lake Kivu (Source: Mininfra, 1998). Parameter

(FGDC) recommends the use of RMSE by using the following relation (Federal Geographic Data Committee, 1998):

Value 2

Catchment area (km ) Surface area (km2) Latitude Longitude Maximum width (km) Length of shore line (km) Maximum water depth (m) Average water depth (m) Volume (109 m3)

7000 2400 1°240 S and 2°300 S 28°500 E and 29°230 E 50 100 485 240 495

To ensure data reliability, gauge readings for a given lake should be retrieved from more than one station. However, not all lakes have more than one gauging station and hence cross checking is restricted. In this study data from four gauging stations (Kamembe, Gisenyi, Rubengera and Rusizi) were used. The bathymetric data of Lake Kivu used in this study were collected from the Ministry of Infrastructures (MININFRA) in Rwanda. The bathymetric study of Lake Kivu was conducted in 1998 by a group of Lahmeyer International and OSAE Companies (Mininfra, 1998). We used the results of this study (Table 1) in combination with satellite images to estimate lake levels of Lake Kivu. In this bathymetric method, the grouping used the bay which is mapped by using a shallow-draft boat equipped with a high-precision Global Positioning System (GPS) coupled with a high-precision depth sounder (Dost and Mannaerts, 2004). Satellite images help in mapping the aerial extent of the lake (Dahdouh-Guebas, 2002). In this study various criteria were applied in selecting the images like the suitability of images and their availability referring to the data quality in relation to the proposed application. This included the spatial resolution and availability of a single scene image that covers the whole lake to avoid the need of making a mosaic of images of different days. We made a mosaic of images that were obtained freely from the Global Land Cover Facility (GLCF) website of University of Maryland (USA) in order to provide a complete coverage of the lake for the same date. Five Landsat (ETM+) images taken on 19/07/1986, 06/12/1999, 03/09/ 2000, 05/06/2002 and 15/07/2005 were imported to the Geographic Resources Analysis Support System (GRASS) and ArcGIS software for processing and analysis. Using these tools, we determined the area of Lake Kivu for these dates after delineating the lake boundary using applicable satellite images. 2.3. Research approach and methodology For the verification of altimetry data and estimating the water level of Lake Kivu, bathymetric data combined with satellite images were used. The research approach is presented in the following flow chart (Fig. 2). A detailed methodology is provided in the following section. 2.3.1. Comparison of lake level data sets The National Standard for Spatial Data Accuracy (NSSDA, US) suggests a statistical methodology for estimating the positional accuracy of points on maps and in digital geospatial data, with respect to geo-referenced ground positions of higher accuracy. In this study altimetry measurements and gauge level readings for Lake Kivu were compared. Gauge level data from the Rusizi station were compared with corresponding altimetry data from ERS-2 and ENVISAT satellites. The Root-Mean-Square-Error (RMSE) and standard deviation for Lake Kivu were calculated for ENVISAT and ERS-2 measurements. As a means of measuring the quality of geospatial data the US Federal Geodetic Control Subcommittee

RMSEz ¼

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 Xn ðZ sat;i  Z g;i Þ2 i¼1 n

ð1Þ

where Zsat,i is the vertical coordinate of the ith check point in the data set (m), Zg,i the vertical coordinate of the ith check point in the independent source of higher accuracy (m), n the number of points being checked (–) and i is integer from 1 to n. Using these procedures vertical accuracy was tested by comparing the elevations in the altimetry data set with elevations of the same points from gauged lake level data set. Referring to the assumption of National Standard for Spatial Data Accuracy, the vertical error was distributed normally and systematic errors were eliminated as best as possible. The factor 1.96 was applied to compute a linear error at the 95% confidence level (Federal Geographic Data Committee, 1998). Therefore, vertical accuracy (Acz) may be computed as:

Acz ¼ 1:96RMSEz

ð2Þ

In this research the data set from gauge measurements were considered as ‘independent source of high accuracy’ for making comparison with altimetry data sets. 2.3.2. Bathymetric data There are three bathymetry studies which have been done for Lake Kivu. The first one was made in 1960 (Capart, 1960), the second in 1978 (Tietze, 1978), and the third one in 1998 (Mininfra, 1998). To set up the bathymetric data, the group of Lahmeyer International and OSAE used a traditional sounder (Atlas Deso 25) at the beginning of measurements, and then they used also an echosounder with one channel (Navisound 50; 210 kHz). The operations of positioning were carried out by means of a receiver Ashtech GG24 for the signals of satellites GPS and GLONASS (Mininfra, 1998). We used morphometric characteristics of the Lake Kivu together with surface areas generated from satellite images to estimate lake water levels, which can be used for ungauged lakes like Lake Kivu (Table 3). 2.3.3. Gauge level estimation Lake level monitoring is a continuous procedure. Ideally, satellite based remote sensing data should provide reliable and cost effective means of water level measurement in lieu of gauging stations. As such it is possible to generate lake levels from satellite imagery and the characteristics of the lake from bathymetric study. The lake levels produced could then be used for verification of radar altimeter data sets where gauge data is not available (Dost and Mannaerts, 2004). This approach can be applied in monitoring lakes that are inaccessible for manual water level measurement but have significant ecological or social relevance. The procedures used in this research to develop the technique are outlined as: (1) Deriving an elevation-area relation using the bathymetric characteristics of the lake. (2) Selecting of suitable satellite images and estimation of lake area from processed images, while single scene satellite images with high resolution were preferred and selected. (3) Correlating area estimated from satellite image and elevation-area characteristic curves. The elevation-area relationship derived from the bathymetry method was used to estimate the lake level gauge using the lake surface area estimated from satellite images. For this purpose,

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Water level monitoring using Radar Remote Sensing data Application to Lake Kivu, Central Africa

Altimetry and gauge data collection

Bathymetry data collection

Data synthesizing

Satellite image retrieval

Image Processing and Analysis

Data synthesizing

Determination of Lake surface area

Comparison of altimetry and gauge lake level data

Determine lake area verses water level relationship

Estimate lake water levels for given surface areas Fig. 2. Flow diagram of research approach.

Gauge-altimetry lake level comparison ENVISAT and ERS-2, Lake Kivu (1995-2005) 1462.5

Water level (m)

1462.0 1461.5 1461.0 1460.5 1460.0 1459.5 1459.0 11 05 2005

22 12 2004

04 08 2004

17 03 2004

20 08 2003

02 04 2003

13 11 2002

26 06 2002

02 01 2002

11 07 2001

21 02 2001

04 10 2000

17 05 2000

29 12 1999

11 08 1999

17 02 1999

26 08 1998

08 04 1998

19 11 1997

02 07 1997

12 02 1997

25 09 1996

03 04 1996

11 10 1995

24 05 1995

1458.5

Date (Day, Month, Year) ENVISTAT altimeter level

ERS-2 altimeter level

Ground gauge level

Fig. 3. Graphical visualization of altimetry-gauge data sets.

suitable satellite images of the lake were used to map the aerial extent of the lake. However, factors affecting the choice of satellite images come into play at this step. LANDSAT images were taken into consideration at this stage due to free availability of these images and the high spatial resolution (30 m  30 m).

ASTER images were not suitable because they do not cover the lake on single date (Berry et al., 2005). This data gap can be filled using histogram-matched data values derived from one or more alternate acquisition dates (USGS, 2006). However, those images should incorporate greater uncertainties in that, and it is not very

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Table 2 Summary of statistical analysis of two missions.

Gauge-Altimetry Correlation for a combination of datasets, Lake Kivu

ENVISAT

ERS-2

Combined ENVISAT and ERS-2

1462.5

RMSE (m) R2 Std. deviation of error (m) Mean lake level (m)

0.254 0.855 0.642 1461.39

0.339 0.769 0.701 1461.26

0.294 0.955 0.669 1461.31

1462.0

Altimeter level (m)

Parameter

different from mosaicing images of different dates, which is the reason to disregard ASTER images. Using digital image processing methods, we determined the surface area of Lake Kivu corresponding to the dates already mentioned.

1461.5

R2 = 0.95 1461.0

1460.5

1460.0

1459.5

3. Results and discussion

1460.0

1460.5

1461.0

1461.5

1462.0

1462.5

Gauge level (m)

3.1. Comparison of altimetry and gauge data

Fig. 5. Combination of data set from ENVISAT and ERS-2 compared to gauge data.

1463.5

Lake level (m)

Lake level data for Lake Kivu from ENVISAT and ERS-2 missions were compared with gauge level data from Rusizi station (Fig. 3). The time span used ranges from 1995 to 2005, and 1995 to 2002 for ENVISAT and ERS-2, respectively. The objective was to use data up 2005 for both missions but data for ERS-2 were only available up to 2002 according to the European Space Agency (ESA), the agency in charge of radar altimetry data collection (Fig. 3). The altimetry data sets from ENVISAT and ERS-2 were compared with the corresponding gauge data. The standard deviations of error for lake level estimates for ENVISAT and ERS-2 data sets are 0.642 m and 0.701 m, respectively. For the combined ENVISAT and ERS-2 data set, the standard deviation of error was estimated to 0.669 m. Fig. 3 shows that the ENVISAT data are closer to gauge data than ERS-2 data. The statistical analysis, which is presented in Table 2, demonstrates that the ENVISAT data set compares better than ERS-2 data sets with the gauge data as shown by a high coefficient of determination (R2), low RMSE and low standard deviation of errors. The coefficients of determination are 86% and 77% for ENVISAT and ERS-2 data sets, respectively. Similarly the RMSE calculated for ENVISAT and ERS-2 data sets are 0.254 m and 0.339 m, respectively (Table 2). The correlations between the different satellite altimetry data sets and the Rusizi gauging station are shown in Fig. 4.

Area characteristics, Lake Kivu (1998; bathymetry data)

1461.5 2

1459.5

R = 0.98

1457.5 1455.5 1453.5 1100

1300

1500

1700

1900

2100

One of the weaknesses of the present day altimetry missions is the limited temporal resolution (about 35 days for both ENVISAT and ERS-2 data sets). But the reported dates of altimetry data from ENVISAT and ERS-2 are different. Therefore, in order to improve the temporal resolution, both of these data sets from 1995 up to 2002 were combined and compared with gauge data sets from Rusizi station. This improved the coefficient of determination to 95% (Fig. 5).

Gauge-Altimetry (ERS-2) level com parison, Lake Kivu

1463.0 1462.5 1462.0

R = 0.85

1460.6

Gauge level (m)

Gauge level (m)

1461.8

2

1461.5 1461.0

1460.0

1459.8

1459.5 1459.4

Altimeter level (m)

R2 = 0.77

1460.5

1460.2

1459.8 1460.2 1460.6 1461.0 1461.4 1461.8 1462.2

2500

Fig. 6. Elevation-surface area characteristics, Lake Kivu (Source: Mininfra, 1998).

1462.2

1461.4

2300

Area (km2)

Gauge-Altimetry (ENVISAT) level comparison, Lake Kivu

1461.0

1463.0

1460.4

1461.4

1462.4

Altimeter level (m)

Fig. 4. Gauge-altimetry correlations for ENVISAT and ERS-2 missions.

1463.4

O. Munyaneza et al. / Physics and Chemistry of the Earth 34 (2009) 722–728 Table 3 Lake gauge level estimation using lake surface area extracted from satellite images. Acquisition date

Area from satellite image (A) (km2)

Gauge level (m)

Lake level from area characteristics curve y = 1.378.1A0.0076 (m above sea level)

19-07-1986 06-12-1999 03-09-2000 05-06-2002 15-07-2005

2370.06 2386.33 2396.07 2384.04 2369.63

1461.95 1461.59 1460.93 1462.11 1461.06

1461.94 1462.01 1462.06 1462.00 1461.94

3.2. Estimation of lake gauge water level The methods used for estimating lake water level explained in section two resulted in a good estimate of lake level with a RMSE of 0.294. A statistical analysis of the error distribution showed that the error is distributed normally (with skewness of 0.434 and standard error of 0.824) and, consequently, the recommendations by Federal Geographic Data Committee (1998) for spatial data accuracy determination can be used. Thus for Lake Kivu, the accuracy of water level estimation with a 95% confidence interval is calculated using Eq. (2) as: ±1.96RMSE = ± 1.96  0.294 m = ± 0.58 m.

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Using morphometric characteristics derived from bathymetry study, we fitted a power curve to check if a reasonably good relationship can be established between the lake surface area and lake water levels (Fig. 6). Fig. 6 shows a power curve fitted to the elevation-area characteristic relationship and gives a coefficient of determination of 0.98. We came up with the following equation which was used for lake level calculation:

y ¼ 1378:1A0:0076

ð3Þ

where y is the Lake water level (m), A is the surface area (km2). Then the surface area of a Lake Kivu was determined from satellite images for each year under study by using digital image processing methods (Table 3). The surface area of the lake from satellite imagery was used to estimate the lake level using the combination of morphometric characteristics of the lake established from bathymetric survey. Fig. 7 shows the image of Lake Kivu obtained after processing and analysis of five Landsat (ETM+) satellite images. The image was used in combination with bathymetry data to estimate lake levels. Other images taken for different years were analysed and processed in a similar way (1986, 1999, 2000 and 2002) but they are not shown in this paper.

Fig. 7. Landsat ETM + satellite images of Lake Kivu (image taken in July 2005) (Source: ESA, 2005).

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Lake level estimations from satellite imagery 1462.3

Gauge level (m)

1462.1 1461.9 R2 = 0.95

1461.7 1461.5 1461.3

data derived from satellites especially in developing countries due to the global coverage and temporal sampling of the processed of satellites data. This method can be used to ensure data continuity. But some limitations were identified during this work such as interpreting radar altimeter measurements made over inland waters. One major drawback of the developed technique is that a single date mosaic images are required. High resolution satellite images of a single date are not easily available and hence lower resolutions images can be applied which actually affects the accuracy of the technique.

1461.1

Acknowledgements 1460.9 1461.9 1462.0 1462.0 1462.0 1462.0 1462.0 1462.1 1462.1

Calculated level (m) Fig. 8. Correlation between measured and calculated lake levels.

The surface area determined from Fig. 7 was used to calculate the elevation of the lake using Eq. (3). This lake elevation value was compared to the gauge level values of the corresponding dates. The results from the bathymetry data and satellite images were combined in order to estimate the lake levels (Table 3). The in situ gauge levels for Lake Kivu were compared with calculated levels using the combination of bathymetry data and satellite images (Table 3). Gauge levels and satellites images were taken on the same dates, and we plotted the chart which is shown bellow to see if the coefficient of determination is acceptable in terms of data quality (Fig. 8). Fig. 8 shows a coefficient of determination of 95% which represents good correlation between measured and calculated lake levels. This shows the capability of the method to generate lake level data for ungauged lake.

4. Conclusions Previous works done in the Amazon River Basin (Brooks, 1982) have shown the considerable potential of inland water and land altimetry application. According to Benveniste and Berry (2004) satellite radar altimeters have the potential to provide accurate height measurements not only for lakes, but also for large rivers such as the Amazon, which has been a primary target of environmental studies over the last 10 years. This conclusion is not different from the findings of this research where altimeter measurements of Lake Kivu showed a good agreement with gauge measurements exhibiting a strong capability for monitoring inland waters. ENVISAT data sets have the merit over ERS-2 data sets due to the lower RMSE and higher temporal resolution. For the ENVISAT data set, a very good gauge-altimeter correlation is observed for Lake Kivu, which may be used to supplement the gauges at the station Rusizi. To achieve an improved temporal resolution, a combination of data sets from ENVISAT and ERS-2 satellite missions were used and compared with gauge level data sets. The elevation-area relationship established in this study is a typical tool for monitoring the lake level and preparing lake management plans. The technique highlighted the feasibility of using

The study was facilitated by UNESCO-IHE Institute for Water Education, the National University of Rwanda (NUR) through the Water Resources and Environmental Management (WREM) Project and the Institute of Scientific and Technological Research (I.R.S.T). Thanks to the WREM project Manager Dr. Innocent Nhapi for his encouragement, financial and material support. Thanks also to the Ministry of Infrastructure and Ministry of Natural Resources (Rwanda) who provided the 1978 and 1998 bathymetric maps of Lake Kivu, and Dr. Richard Smith from UK who kindly made the altimetry signals processing and provided the altimetry data used in the research. References Abebe, B., 1999. To upgrade the hydro-meteorological network in Ethiopia. In: 25th WEDC Conference, Addis Ababa. Benveniste, J., Berry, P., 2004. Monitoring river and lake levels from space. ESA bulletin 117, February 2004. Berry, P.A.M., Garlick, J.D., Freeman, J.A., Pinnock, R.A., 2005. Development of algorithms for the exploitation of ERS-ENVISAT altimetry for the generation of a river and lake product, p. 4. Brooks, R.L., 1982. Lake elevation from satellite radar altimetry from a validation area in Canada. Geosci. Res. Corp., Salisbury, Maryland, USA, November 1982. Capart, A., 1960. Etude Bathymétrique du Lac Kivu. Bathymetric study of Lake Kivu. MININFRA, Kigali, Rwanda (in French). CIT, C.I.o.T., 2006. Altimeter Ocean Pathfinder. , November 2006. Dahdouh-Guebas, F., 2002. Remote Sensing and GIS in the Sustainable Management of Tropical Coastal Ecosystems, Environment. Development and Sustainability. Kluwer Academic Publishers, Dordrecht, The Netherlands. pp. 93–112. Special issue. vol. 4(2). Dost, R.J.J., Mannaerts, C.M.M., 2004. Bathymetry generation using sonar and satellite imagery, poster presented at AARSE. ESA., 2005. Lake and River product: Altimeter Coverage over Lake Kivu. European Space Agency, (accessed July 2007). Federal Geographic Data Committee., 1998. Geospatial Positioning Accuracy Standards, Part 3: National Standard for Spatial Data Accuracy, Federal Geographic Data Committee, Secretariat, USA. Fekete, B.M., Vorosmarty, C.J., 2006. The current status of global river discharge monitoring. , November 2006. GRDC., 2006. Temporal Distribution of Daily Data Stations. (accessed January 2007). Mininfra., 1998. Levé Bathymétrique complet du Lac Kivu. Groupement Lahmeyer International/OSAE, Kigali. NASA., 2005. Radar Altimetry. (accessed August 2006). NELSAP., 2006. Natural Resources Management and Development. Nile Equatorial Lakes Subsidiary Action Program (NELSAP), Kigali, Rwanda. Rosmorduc, V., Benveniste, J., Lauret, O., Milagro, M., Picot, N., 2006. Radar Altimetry Tutorial. , December 2006. Tietze, K., 1978. Etude Bathymétrique du Lac Kivu. Bathymetric study of Lake Kivu. MININFRA, Kigali, Rwanda (in French).